Field of Disclosure
Embodiments described herein generally relate to a framework for encoding related, weighted, ordered arrangements of data as a sub-symbolic code. The sub-symbolic code provides a seamless framework for performing operations such as searching, indexing, clustering, and data transformation and/or data translation.
Description of Related Art
The background description provided herein is for the purpose of generally presenting the context of the disclosure. Work of the presently named inventors, to the extent the work is described in this background section, as well as aspects of the description that may not otherwise qualify as prior art at the time of filing, are neither expressly nor impliedly admitted as prior art against the present disclosure.
High-dimensional data is difficult to encode and interpret. One approach is to simplify the data by assuming that the data of interest lies on an embedded non-linear manifold within a higher-dimensional space. If the manifold is of a low enough dimension, then the data can be visualized in the low dimensional space. However, all of the currently available data processing techniques require (and thereby assume) that the spaces are homogenous, and that only one manifold per space exist.
Furthermore, all of the currently available data processing techniques use some form of underlying proximity matrices and traditional vector space approaches such as latent semantic analysis, principle components analysis, multidimensional scaling, neural networks, as well as variants of all the preceding approaches to process the data. Moreover, a major drawback of such data processing methods is that ordered relationships between data are made as symmetric distance measurements. Thus, in the framework of such data processing techniques, the original order dependent properties of data are lost. For instance, statements like “the man bit the dog” are indiscernible from statements like “the dog bit the man”.
Accordingly, there is a requirement for a framework that can represent and process data relationships in a manner, wherein the framework supports multiple manifolds in possibly heterogeneous spaces, and wherein each manifold or plurality of manifolds may have a unique attitude, orientation, and stance within the higher dimensional space.